Spark多类分类示例 [英] Spark Multiclass Classification Example

问题描述

你们知道我在哪里可以找到Spark中的多类分类示例.我花了很多时间在书籍和网络上进行搜索，到目前为止，我只知道从文档的最新版本开始是有可能的.

Do you guys know where can I find examples of multiclass classification in Spark. I spent a lot of time searching in books and in the web, and so far I just know that it is possible since the latest version according the documentation.

推荐答案

ML

(在Spark 2.0+中推荐)

我们将使用与下面的MLlib中相同的数据.有两个基本选项.如果Estimator支持开箱即用的多类分类(例如随机森林)，则可以直接使用它:

We'll use the same data as in the MLlib below. There are two basic options. If Estimator supports multilclass classification out-of-the-box (for example random forest) you can use it directly:

val trainRawDf = trainRaw.toDF

import org.apache.spark.ml.feature.{Tokenizer, CountVectorizer, StringIndexer}
import org.apache.spark.ml.Pipeline

import org.apache.spark.ml.classification.RandomForestClassifier

val transformers = Array(
  new StringIndexer().setInputCol("group").setOutputCol("label"),
  new Tokenizer().setInputCol("text").setOutputCol("tokens"),
  new CountVectorizer().setInputCol("tokens").setOutputCol("features")
)


val rf = new RandomForestClassifier() 
  .setLabelCol("label")
  .setFeaturesCol("features")

val model = new Pipeline().setStages(transformers :+ rf).fit(trainRawDf)

model.transform(trainRawDf)

如果模型仅支持二进制分类(逻辑回归)并扩展了o.a.s.ml.classification.Classifier，则可以使用一对多休息"策略:

If model supports only binary classification (logistic regression) and extends o.a.s.ml.classification.Classifier you can use one-vs-rest strategy:

import org.apache.spark.ml.classification.OneVsRest
import org.apache.spark.ml.classification.LogisticRegression

val lr = new LogisticRegression() 
  .setLabelCol("label")
  .setFeaturesCol("features")

val ovr = new OneVsRest().setClassifier(lr)

val ovrModel = new Pipeline().setStages(transformers :+ ovr).fit(trainRawDf)

MLLib

目前根据官方文档 (MLlib 1.6.0)以下方法支持多类分类:

According to the official documentation at this moment (MLlib 1.6.0) following methods support multiclass classification:

• 逻辑回归，
• 决策树
• 随机森林
• 朴素贝叶斯

至少某些示例使用多类分类:

At least some of the examples use multiclass classification:

• 朴素贝叶斯示例-3个类
• 逻辑回归-10类分类器，尽管示例数据中只有2个

• Naive Bayes example - 3 classes
• Logistic regression - 10 classes for classifier although only 2 in the example data

忽略方法特定参数的通用框架与MLlib中的所有其他方法几乎相同.您必须对输入进行预处理才能创建带有表示label和features的列的任一数据框:

General framework, ignoring method specific arguments, is pretty much the same as for all the other methods in MLlib. You have to pre-processes your input to create either data frame with columns representing label and features:

root
 |-- label: double (nullable = true)
 |-- features: vector (nullable = true)

或RDD[LabeledPoint].

Spark提供了广泛的有用工具，旨在促进此过程，包括功能提取器和功能变压器和管道.

Spark provides broad range of useful tools designed to facilitate this process including Feature Extractors and Feature Transformers and pipelines.

您会在下面找到一个使用随机森林"的简单例子.

You'll find a rather naive example of using Random Forest below.

首先让我们导入所需的程序包并创建虚拟数据:

First lets import required packages and create dummy data:

import sqlContext.implicits._
import org.apache.spark.ml.feature.{HashingTF, Tokenizer} 
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.ml.feature.StringIndexer
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.linalg.{Vectors, Vector}
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.sql.Row
import org.apache.spark.rdd.RDD

case class LabeledRecord(group: String, text: String)

val trainRaw = sc.parallelize(
    LabeledRecord("foo", "foo v a y b  foo") ::
    LabeledRecord("bar", "x bar y bar v") ::
    LabeledRecord("bar", "x a y bar z") ::
    LabeledRecord("foobar", "foo v b bar z") ::
    LabeledRecord("foo", "foo x") ::
    LabeledRecord("foobar", "z y x foo a b bar v") ::
    Nil
)

现在让我们定义所需的变压器和过程列Dataset:

Now let's define required transformers and process train Dataset:

// Tokenizer to process text fields
val tokenizer = new Tokenizer()
    .setInputCol("text")
    .setOutputCol("words")

// HashingTF to convert tokens to the feature vector
val hashingTF = new HashingTF()
    .setInputCol("words")
    .setOutputCol("features")
    .setNumFeatures(10)

// Indexer to convert String labels to Double
val indexer = new StringIndexer()
    .setInputCol("group")
    .setOutputCol("label")
    .fit(trainRaw.toDF)


def transfom(rdd: RDD[LabeledRecord]) = {
    val tokenized = tokenizer.transform(rdd.toDF)
    val hashed = hashingTF.transform(tokenized)
    val indexed = indexer.transform(hashed)
    indexed
        .select($"label", $"features")
        .map{case Row(label: Double, features: Vector) =>
            LabeledPoint(label, features)}
}

val train: RDD[LabeledPoint] = transfom(trainRaw)

请注意，indexer已适合"火车数据.它仅表示将用作标签的分类值转换为doubles.要对新数据使用分类器，您必须先使用此indexer对其进行转换.

Please note that indexer is "fitted" on the train data. It simply means that categorical values used as the labels are converted to doubles. To use classifier on a new data you have to transform it first using this indexer.

接下来，我们可以训练RF模型:

Next we can train RF model:

val numClasses = 3
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 10
val featureSubsetStrategy = "auto"
val impurity = "gini"
val maxDepth = 4
val maxBins = 16

val model = RandomForest.trainClassifier(
    train, numClasses, categoricalFeaturesInfo, 
    numTrees, featureSubsetStrategy, impurity,
    maxDepth, maxBins
)

最后进行测试:

val testRaw = sc.parallelize(
    LabeledRecord("foo", "foo  foo z z z") ::
    LabeledRecord("bar", "z bar y y v") ::
    LabeledRecord("bar", "a a  bar a z") ::
    LabeledRecord("foobar", "foo v b bar z") ::
    LabeledRecord("foobar", "a foo a bar") ::
    Nil
)

val test: RDD[LabeledPoint] = transfom(testRaw)

val predsAndLabs = test.map(lp => (model.predict(lp.features), lp.label))
val metrics = new MulticlassMetrics(predsAndLabs)

metrics.precision
metrics.recall

这篇关于Spark多类分类示例的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！